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Bio-Cell Image Segmentation based on Deep Learning using Denoising Autoencoder and Graph Cuts

디노이징 오토인코더와 그래프 컷을 이용한 딥러닝 기반 바이오-셀 영상 분할

  • Lim, Seon-Ja (Dept. of IT Convergence and Application Eng., Pukyong National University) ;
  • Vununu, Caleb (Dept. of IT Convergence and Application Eng., Pukyong National University) ;
  • Kwon, Oh-Heum (Dept. of IT Convergence and Application Eng., Pukyong National University) ;
  • Lee, Suk-Hwan (Dept. of Computer Eng., Dong-A University) ;
  • Kwon, Ki-Ryoug (Dept. of IT Convergence and Application Eng., Pukyong National University)
  • 임선자 ;
  • 칼렙부누누 ;
  • 권오흠 ;
  • 이석환 ;
  • 권기룡
  • Received : 2021.07.12
  • Accepted : 2021.10.06
  • Published : 2021.10.30

Abstract

As part of the cell division method, we proposed a method for segmenting images generated by topography microscopes through deep learning-based feature generation and graph segmentation. Hybrid vector shapes preserve the overall shape and boundary information of cells, so most cell shapes can be captured without any post-processing burden. NIH-3T3 and Hela-S3 cells have satisfactory results in cell description preservation. Compared to other deep learning methods, the proposed cell image segmentation method does not require postprocessing. It is also effective in preserving the overall morphology of cells and has shown better results in terms of cell boundary preservation.

Keywords